January 2008

Radiology Rays' Second Season

In their second season at bat, the Radiology Rays moved up in the rankings from 2006, finishing 17th out of 24 teams with a total of 74 runs, 3 wins, and 5 losses (http://www.sumcsoftball.com/2007/scores/standings.asp). This represents a substantial improvement over their first season in 2006 when the Radiology Rays finished 24th out of 25 teams with a total of 30 runs, 1 win, and 7 losses (http://www.sumcsoftball.com/2006/scores/standings.asp). Most of their losses this season were close games in which the Rays lost by one or two runs compared to 2006 when the gap in runs was much greater. This season, their three wins were exciting and close games in which the Rays came from behind to win by a one- or two-run victory over their opponents. One of their three winning games was won in the last swing in the bottom of the last inning of the game! To view pictures from their 2007 season taken by Dr. Sandip Biswal, please access http://www.kodakgallery.com/Slideshow.jsp?Uc=eemwbyj.4ayvpx3b&Uy=-wkrzcz&Upost_signin=Slideshow.jsp%3Fmode%3Dfromshare&Ux=0&mode=fromshare&conn_speed=1.

The Rays are gearing up for the 2008 season and welcome anyone who would like to join them! Please contact . . .

Dr. Fred Chinn at chinf@stanford.edu for more information on the Rays' upcoming 2008 softball season!

A) 2007 Final Game Scores: The Rays (http://www.sumcsoftball.com/2007/scores/team.asp?team=The%20Rays)

6/6/2007; Wed. 5:30; Roadies 11; The Rays 10

6/13/2007; Wed. 5:30; Cha-Ching 9; The Rays 10

6/21/2007; Thu.; 5:30; Heart Breakers 12; The Rays 11

7/5/2007; Thu.; 5:30; Radicals 17; The Rays 13

7/10/2007; Tue. 5:30; Traumatizers 14; The Rays 16

7/18/2007; Wed. 5:30; Fill N Kill 17; The Rays 8

7/26/2007; Thu. 5:30; MSOB's 20; The Rays 1

8/2/2007; Thu. 5:30; Rainmakers 4; The Rays 5

B) 2006 Final Game Scores: The Rays (http://www.sumcsoftball.com/2006/scores/team.asp?team=The%20Rays)

6/1/2006; Thu. 5:30; Roadies 22; The Rays 1

6/8/2006; Thu. 6:45; Jerks 26; The Rays 2

6/13/2006; Tue. 6:45; Radicals 24; The Rays 4

6/21/2006; Wed. 5:30; Leaches 9; The Rays 3

7/5/2006; Tue. 6:45; Cardiology 31; The Rays 7

7/12/2006; Wed. 6:45; BC Bombers 0; The Rays 5

7/18/2006; Tue. 6:45; Code White 15; The Rays 5

7/25/2006; Tue. 5:30; Radiology 19; The Rays 3

Visiting Faculty: January 31, 2008

Hadassa Degani, PhD, is a recent visitor to our Department and on sabbatical from the Weizmann Institute of Science in Israel. Professor Degani has also spent a sabbatical year at Yale University and at the Pasteur Institute, as well as summers at the University of Oxford; University of the UK; Fox Chase Cancer Center in Philadelphia; and the University of Pennsylvania, where she also serves as an adjunct professor. She is known internationally for her work in MR imaging of breast cancer and, more recently, for her work on prostate cancer. She received a BSc in chemistry from the Hebrew University of Jerusalem in 1966, an MSc in physical chemistry from the Weizmann Institute of Science in 1969, and a PhD in chemistry from the State University of New York at Stony Brook in 1974. Her postdoctoral research was carried out both at Stony Brook and at the University of Tel-Aviv. In 1976, she joined the staff of the Weizmann Institute; she currently serves as a full professor in the Department of Biological Regulation and the Willner Family Center for Vascular Biology. Professor Degani is the incumbent of the Fred and Andrea Fallek Professorial Chair for Breast Cancer Research.

Professor Degani's research focuses on the development of magnetic resonance imaging and spectroscopy in biomedical research and the integration of these state-of-the-art methodologies with modern molecular biology. Specifically, her research centers on the hormonal regulation of breast cancer, as well as the role of blood vessels in the progression and metastasis of this malignancy. She and her colleagues use magnetic resonance to detect and diagnose breast and prostate cancer and to monitor the effectiveness of cancer therapy. For more information on her research and publications, please access http://www.weizmann.ac.il/Biological_Regulation/degani/. Professor Hadassa Degani and her husband, Dr. Gabriel D. Degani, have two daughters and a son; they also enjoy being grandparents to three grandchildren.

Galyna Pecherska and Maureen Wong Participate in the 7th Annual Susan G. Komen Race for the Cure

Galyna Pecherska, CPC-A, coder, Maureen Wong, financial management analyst, and her son, Sam, participated in the 7th Annual Susan G. Komen San Francisco Race for the Cure on Sunday, September 23, 2007, from 7:30 AM to 1:00 PM beginning near the Ferry Building in San Francisco. You can view pictures from their race taken by Maureen at http://share.shutterfly.com/action/welcome?sid=0AaNG7ZqzbN2L74. Sponsored by the Stanford Cancer Center/Northern California Cancer Center, this 5K Run/Walk or one-mile Fun Walk raised funds and awareness for the fight against breast cancer while celebrating breast cancer survivorship and honoring those who have lost their battle with the disease. The Susan G. Komen national organization provides enormous financial support for breast cancer research, novel clinical trials, and the training of future breast cancer specialists through a dedicated fellowship program at many cancer centers around the U.S., including Stanford. The national organization is closely linked with the San Francisco affiliate office, which provides support services for numerous organizations in the greater Bay Area region such as Meals on Wheels, free mammograms for low-income women, and the Community Breast Health Project in Palo Alto. To register for future races and walks, please see http://www.sfkomen.org/.

New Faculty Hires and Promotions: January 23, 2008

Zhen Cheng, PhD, became an assistant professor (research) of radiology and member of the Molecular Imaging Program at Stanford (MIPS) in September of 2007. He was an undergraduate at Sichuan University, where he received his Bachelor of Science degree in chemistry. Dr. Cheng also holds an MS from the National Research Center of Isotope Engineering and Technology & China Institute of Atomic Energy and a PhD from the Department of Chemistry at the University of Missouri-Columbia. From 2001 to 2003, he was a postdoctoral fellow in the Department of Radiology at Harvard Medical School. His awards include a Young Investigator Travel Scholarship to attend the 2005 Academy of Molecular Imaging Annual Conference and a 1997-1998 graduate fellowship at the University of Missouri-Columbia. Dr. Cheng is currently a member of the Cancer Molecular Imaging Chemistry Laboratory (CMICL) of MIPS where he is developing novel molecular imaging probes and non-invasive techniques for the early detection of cancer and its metastasis. He is also researching the molecular, metabolic, and physiological characteristics of cancers and their responses to therapy by identifying novel cancer biomarkers with significant clinical relevance; by devising new chemistry for the preparation of probes; and by validating new strategies for probes by using high-throughput screening.

(Image courtesy of Mark Riesenberger)

New Faculty Hires and Promotions: January 23, 2008

Aya Kamaya, MD, was appointed as an assistant professor in the abdominal imaging section of the Radiology Department on October 1, 2007. Since the completion of her fellowship in body imaging at Stanford in 2005, she has been a clinical instructor and clinical assistant professor in the abdominal imaging section at Stanford. During this time, she was given two teaching awards for her outstanding contributions to resident education, compassionate patient care, and research. She is currently the assistant fellowship director of the Stanford Body Imaging Fellowship. Prior to coming to Stanford for her fellowship, she completed her residency in diagnostic radiology at the University of Michigan, Ann Arbor, where she was awarded the Executive Council Award from the American Roentgen Ray Society for her work on "Color Doppler Twinkling Artifact" and the Laurence A. Mack Research Award from the Society of Radiologists in Ultrasound for her work on "Linear Streak Artifact." She completed medical school at the University of Utah in her hometown of Salt Lake City. As an undergraduate, she double majored in engineering sciences and Asian Studies, securing her two bachelor's degrees at Dartmouth College in Hanover, New Hampshire. Her research interests include investigating new ultrasound technologies such as photoacoustic ultrasound, in conjunction with the Electrical Engineering Department at Stanford; liver imaging; and women's imaging. Outside of work, her favorite activities include skiing and snowboarding through powder (her favorite ski resort is Snowbird, UT), as well as running at the Stanford Dish, surfing, and traveling.

(Image courtesy of Mark Riesenberger)

Announcements IV: January 23, 2008

Special Seminar Series on Radiological Informatics: As part of a special series on radiological informatics, we are offering seminars on Jan. 23rd, 24th, 28th, and 31st. Please click on the "Continue Reading This Entry" link below to find the title of each talk as well as the presenter's abstract and biography. For more information, please contact Dr. Sandy Napel.

1) Wednesday, January 23rd, at noon; Alway M104
Julia Patriarche, PhD
Mayo Clinic

Title:
"Detection of Change in Serial Magnetic Resonance Studies of Brain Tumor Patients"

Abstract:
The comparison of serial magnetic resonance imaging studies is a common task in clinical radiology. It is, however, widely considered not to be very reproducible. There are a variety of reasons for this, including the confounding of disease-related changes with acquisition-related changes and issues related to information presentation. We have constructed a computational system that performs the comparison of serial magnetic resonance imaging studies and presents changes in the form of a color-coded change map, superimposed on the anatomical images. The system additionally formats the output as a quantitative summary. We used this quantitative summary to conduct a study with 88 brain tumor serial comparisons. Our results were suggestive that it may be possible to use the change detector to identify cancer changes months earlier than is possible using manual inspection, alone.

We have recently implemented an integrated system for the change detector, which includes a graphical user interface (GUI). The GUI not only displays the color-coded change map, but also allows the user to turn it on and off. The GUI provides linked cursors, and it additionally provides "flicker" functionality to allow the user to rapidly alternate between the serial acquisitions. We are preparing to deploy the GUI change detector clinically, which will greatly increase the size and variety of possible future research studies and which will allow the direct clinical application of this technology.

The change detector is an example of a layered artificial intelligence (AI) architecture in which each layer builds upon the layer below, with each layer accomplishing progressively more sophisticated analyses. Specifically, the change detector is built on a lesion-finder application. The lesion finder is built on an automated sample point's algorithm. The automated sample point's algorithm is built on a significant region detection algorithm. Each of these algorithms has merit in its own right, and each can be used in a modular fashion in a variety of contexts. As a unified application, they together automatically address a complex clinical task. Early detection of changes may facilitate improved care through more rapid intervention following recurrence. It may also facilitate screening and personalized therapy. We additionally see the change detector as providing a solution to the problem of novel therapy comparison, by providing fully automatic, reproducible, and quantitative measures of change. We envision the change detector as a model of layered artificial intelligence, not only freeing the radiologist from the drudgery of information overload, but providing a model whereby greater information will enable many sophisticated automatic analyses by the computer, with the computer bringing to the attention of the clinician only what is relevant.

Biography:
Julia Patriarche is an informatics fellow in the Radiology Informatics Lab at the Mayo Clinic College of Medicine. She has completed an undergraduate degree in electrical engineering/computer engineering option at Queen's University in Kingston, Canada; a PhD in medical science/medical imaging; and a neurology fellowship at the Mayo Clinic College of Medicine.

2) Thursday, January 24th, at noon; Alway M112
Ross Mitchell, PhD
University of Calgary

Title:
"Virtual Biopsies: Non-Invasive Molecular Diagnosis"

Abstract:
Our expanding knowledge of the genetic basis and molecular mechanisms of cancer is beginning to revolutionize the practice of clinical oncology. Increasingly, molecular biomarkers of prognosis and treatment response are being used to classify tumors and direct treatment decisions. Advanced medical imaging platforms such as MRI, PET, and CT provide incredibly detailed images of tumors that reflect their structure, biochemistry, physiology, and perhaps genetics.

Studies by the Imaging Informatics Lab at the University of Calgary, and others, show that information about a tumor's molecular phenotype can be obtained by using novel algorithms and computational tools to more fully analyze tumor images. Such "virtual biopsies," performed by applying these image-processing techniques to routine diagnostic images (e.g. MRI, PET, or CT), could be a rapid and powerful means of assaying important cancer biomarkers. If successfully validated, and proven to have suitable sensitivity and specificity, the use of non-invasive, imaging-based molecular diagnostic tests would offer significant advantages over conventional surgical biopsies. For example, this could be important in the context of large heterogeneous tumors, multiple metastases, surgically inaccessible tumors, and settings where disease progression needs to be monitored frequently over time. Virtual biopsy research lies at the intersection of molecular imaging, medical imaging physics, and biocomputation, and is highly complementary to these areas. This presentation will cover key enabling technologies behind virtual biopsies and discuss some recent successes in this research.

Biography:
Dr. Ross Mitchell is an associate professor of the Departments of Radiology and Clinical Neurosciences and an adjunct professor of the Department of Computer Science at the University of Calgary. He is also the founding and chief scientist of Calgary Scientific Incorporated, a Multiple Sclerosis Society of Canada; a Donald Paty Scholar; and an Alberta Heritage Foundation for Medical Research Senior Scholar. Dr. Mitchell has received numerous awards for his research including the Berlex Canada MS Research Award; several Dean's Awards of Excellence from the University of Western Ontario; Best Paper Awards from the Canadian Association of Radiologists and the International Organization for Medical Physics; and two Awards of Merit from the Radiological Society of North America. Dr. Mitchell has a proven research track-record comprising 11 patents, 73 invited presentations, 63 peer-reviewed articles, and 150 published abstracts.

Dr. Mitchell supervises a research team investigating space/frequency analysis, medical image processing, as well as segmentation and visualization technologies. For more information, please see, http://www.ImagingInformatics.ca.

3) Monday, January 28th, at noon; Alway M104
Jianming Liang, PhD
Siemens Medical Solutions USA Inc., Malvern, PA

Title:
"Dynamic Chest Image Analysis, United Snakes, and
Computer-Aided Detection"

Abstract:
Modern medical imaging systems generate enormous datasets with ever higher coverage and resolution, but it is the clinically relevant information in these images that is paramount. I shall present several novel computational approaches for gleaning such information from chest X-ray images to reveal pulmonary functional abnormalities, for segmenting and characterizing organ motions, and for detecting the most lethal diseases from CT images, including pulmonary embolism and colonic polyps. The former approach has yielded model-based analysis and visualization methods for revealing focal and general abnormalities of lung ventilation and perfusion based on a sequence of digital chest fluoroscopy frames collected with the dynamic pulmonary imaging (DPI) technique.

In particular, I shall present a novel multiresolutional method with an explicit ventilation/perfusion analysis model, as well as "United Snakes," an interactive deformable model framework for lung registration and motion analysis, cardiac shape and motion analysis, and other applications. Finally, I will introduce a fast yet effective concentration-oriented tobogganing technique for efficient local artery/vein separation and multiple instance classification for the automated detection of pulmonary embolism from CT pulmonary angiography (CTPA), and a virtual colonoscopy technique that simplifies the complex 3D-polyp detection problem into a 2D-disk identification problem, significantly improving sensitivity while reducing computation time.

Biography:
Dr. Jianming Liang is a staff scientist at Siemens Medical Solutions USA, Inc., where he has been engaged in research and development activities in the domain of computer-aided diagnosis in medical imaging since December 2002. He holds a PhD degree (2001) in computer science and carried out his thesis work at the Turku Centre for Computer Science in Finland and in the Visual Modeling Group at the University of Toronto in Canada. From 2001-02, he was a Natural Sciences and Engineering Research Council (NSERC) of Canada Industrial Research Fellow. His research on dynamic chest image analysis received a University Faculty Research Award from the University of Turku. His other prizes include a Siemens Recognition Award and a Best Paper Award at the 2007 International Congress of Computer Assisted Radiology and Surgery in Berlin, Germany.

4) Thursday, January 31st, at noon; Clark Center Auditorium
Daniel Rubin, MS, MD
Stanford University

Title:
"Imaging Informatics: From Bench to Bedside and Beyond"

Abstract:
Vast amounts of knowledge lie within the grasp of radiology researchers and practitioners to help them to understand disease and to practice effectively, but much current biomedical knowledge is not being accessed and utilized. The explosion in images and non-imaging data is challenging the ability of radiology researchers to identify and to pursue promising new investigational directions. The latest results that researchers produce are not always informing radiologists in their day-to-day work, as there are few tools to help them to identify, retrieve, and use pertinent clinical and research knowledge at the point of care. Consequently, there is variability among radiologists in their clinical effectiveness, and opportunities for translating new discoveries into practice are being lost. The methods and tools of biomedical informatics are enabling biologists to cope with similar problems arising from the information explosion in biology, and they are adopting informatics techniques to function effectively in the e-Science era.

In this presentation, I will discuss ongoing work to develop and apply biomedical informatics techniques to meet the information challenges in radiology. Specifically, knowledge representation, semantic annotation, statistical natural language processing, data integration/warehousing, computer reasoning, and decision support are key directions in informatics needed to create intelligent applications for radiology. Future advances in radiology will lie at the intersection of imaging science and biomedical informatics. The new computer applications that emerge will change clinical imaging workstations into knowledge portals and enable radiologists to keep pace with new discoveries, to exploit new radiology knowledge, and to practice more consistently and effectively.

Biography:
Daniel Rubin is a research scientist in the Center for Biomedical Informatics Research and clinical assistant professor of radiology at Stanford University. He is director of scientific development for the National Center for Biomedical Ontology, a National Center for Biomedical Computing of the NIH Roadmap. He is chair of the RadLex Steering Committee of RSNA, chair of the Informatics Committee of the American College of Radiology Imaging Network (ACRIN), and co-chair of the Medical Imaging Systems Working Group of the American Medical Informatics Association. In addition to informatics, his background includes clinical and investigational radiology, as a radiologist and researcher. His academic focus is the intersection of biomedical informatics and imaging science, developing computational methods and applications to access and integrate diverse clinical and imaging data, to extract information and meaning from images, to enable data mining and discovery of image biomarkers, and to translate these methods into practice by creating computer applications that will improve diagnostic accuracy and clinical effectiveness.

New Faculty Hires and Promotions: January 23, 2008

Lewis Shin, MD, will be starting as an assistant professor of diagnostic radiology in February 2008. He has been a clinical instructor in our Department since August of 2007 after completing a body imaging fellowship from 2005 to 2007 through our Advanced Techniques for Cancer Imaging Program, which is funded by the National Cancer Institute. Prior to coming to Stanford, he attended Brown University where he received his BS in neuroscience in 1996 and his medical degree in 2000. Dr. Shin completed his internship and residency in diagnostic radiology at Winthrop University Hospital in Mineola, New York. His research interests include real time MRI airway imaging and body imaging, specifically diffusion-weighted imaging and virtual colonoscopy with CT and MR. Born and raised in New York, his hobbies
include ice hockey and golf.

(Image courtesy of Mark Riesenberger)

Announcements III: January 22, 2008

Special Seminar Series on Radiological Informatics: As part of a special series on radiological informatics, we are offering seminars on Jan. 23rd, 24th, 28th, and 31st. Please click on the "Continue Reading This Entry" link below to find the title of each talk as well as the presenter's abstract and biography. In addition to the seminars listed below, there will be a few more talks, which are being scheduled now and will be posted shortly. Please contact Dr. Sandy Napel for more information.

1) Wednesday, January 23rd, at noon; Alway M104
Julia Patriarche, PhD
Mayo Clinic

Title:
"Detection of Change in Serial Magnetic Resonance Studies of Brain Tumor Patients"

Abstract:
The comparison of serial magnetic resonance imaging studies is a common task in clinical radiology. It is, however, widely considered not to be very reproducible. There are a variety of reasons for this, including the confounding of disease-related changes with acquisition-related changes and issues related to information presentation. We have constructed a computational system that performs the comparison of serial magnetic resonance imaging studies and presents changes in the form of a color-coded change map, superimposed on the anatomical images. The system additionally formats the output as a quantitative summary. We used this quantitative summary to conduct a study with 88 brain tumor serial comparisons. Our results were suggestive that it may be possible to use the change detector to identify cancer changes months earlier than is possible using manual inspection, alone.

We have recently implemented an integrated system for the change detector, which includes a graphical user interface (GUI). The GUI not only displays the color-coded change map, but also allows the user to turn it on and off. The GUI provides linked cursors, and it additionally provides "flicker" functionality to allow the user to rapidly alternate between the serial acquisitions. We are preparing to deploy the GUI change detector clinically, which will greatly increase the size and variety of possible future research studies and which will allow the direct clinical application of this technology.

The change detector is an example of a layered artificial intelligence (AI) architecture in which each layer builds upon the layer below, with each layer accomplishing progressively more sophisticated analyses. Specifically, the change detector is built on a lesion-finder application. The lesion finder is built on an automated sample point's algorithm. The automated sample point's algorithm is built on a significant region detection algorithm. Each of these algorithms has merit in its own right, and each can be used in a modular fashion in a variety of contexts. As a unified application, they together automatically address a complex clinical task. Early detection of changes may facilitate improved care through more rapid intervention following recurrence. It may also facilitate screening and personalized therapy. We additionally see the change detector as providing a solution to the problem of novel therapy comparison, by providing fully automatic, reproducible, and quantitative measures of change. We envision the change detector as a model of layered artificial intelligence, not only freeing the radiologist from the drudgery of information overload, but providing a model whereby greater information will enable many sophisticated automatic analyses by the computer, with the computer bringing to the attention of the clinician only what is relevant.

Biography:
Julia Patriarche is an informatics fellow in the Radiology Informatics Lab at the Mayo Clinic College of Medicine. She has completed an undergraduate degree in electrical engineering/computer engineering option at Queen's University in Kingston, Canada; a PhD in medical science/medical imaging; and a neurology fellowship at the Mayo Clinic College of Medicine.

2) Thursday, January 24th, at noon; Alway M112
Ross Mitchell, PhD
University of Calgary

Title:
"Virtual Biopsies: Non-Invasive Molecular Diagnosis"

Abstract:
Our expanding knowledge of the genetic basis and molecular mechanisms of cancer is beginning to revolutionize the practice of clinical oncology. Increasingly, molecular biomarkers of prognosis and treatment response are being used to classify tumors and direct treatment decisions. Advanced medical imaging platforms such as MRI, PET, and CT provide incredibly detailed images of tumors that reflect their structure, biochemistry, physiology, and perhaps genetics.

Studies by the Imaging Informatics Lab at the University of Calgary, and others, show that information about a tumor's molecular phenotype can be obtained by using novel algorithms and computational tools to more fully analyze tumor images. Such "virtual biopsies," performed by applying these image-processing techniques to routine diagnostic images (e.g. MRI, PET, or CT), could be a rapid and powerful means of assaying important cancer biomarkers. If successfully validated, and proven to have suitable sensitivity and specificity, the use of non-invasive, imaging-based molecular diagnostic tests would offer significant advantages over conventional surgical biopsies. For example, this could be important in the context of large heterogeneous tumors, multiple metastases, surgically inaccessible tumors, and settings where disease progression needs to be monitored frequently over time. Virtual biopsy research lies at the intersection of molecular imaging, medical imaging physics, and biocomputation, and is highly complementary to these areas. This presentation will cover key enabling technologies behind virtual biopsies and discuss some recent successes in this research.

Biography: Dr. Ross Mitchell is an associate professor of the Departments of Radiology and Clinical Neurosciences and an adjunct professor of the Department of Computer Science at the University of Calgary. He is also the founding and chief scientist of Calgary Scientific Incorporated, a Multiple Sclerosis Society of Canada; a Donald Paty Scholar; and an Alberta Heritage Foundation for Medical Research Senior Scholar. Dr. Mitchell has received numerous awards for his research including the Berlex Canada MS Research Award; several Dean's Awards of Excellence from the University of Western Ontario; Best Paper Awards from the Canadian Association of Radiologists and the International Organization for Medical Physics; and two Awards of Merit from the Radiological Society of North America. Dr. Mitchell has a proven research track-record comprising 11 patents, 73 invited presentations, 63 peer-reviewed articles, and 150 published abstracts.

Dr. Mitchell supervises a research team investigating space/frequency analysis, medical image processing, as well as segmentation and visualization technologies. For more information, please see, http://www.ImagingInformatics.ca.

3) Monday, January 28th, at noon; Alway M104
Jianming Liang, PhD
Siemens Medical Solutions USA Inc., Malvern, PA

Title:
"Dynamic Chest Image Analysis, United Snakes, and
Computer-Aided Detection"

Abstract:
Modern medical imaging systems generate enormous datasets with ever higher coverage and resolution, but it is the clinically relevant information in these images that is paramount. I shall present several novel computational approaches for gleaning such information from chest X-ray images to reveal pulmonary functional abnormalities, for segmenting and characterizing organ motions, and for detecting the most lethal diseases from CT images, including pulmonary embolism and colonic polyps. The former approach has yielded model-based analysis and visualization methods for revealing focal and general abnormalities of lung ventilation and perfusion based on a sequence of digital chest fluoroscopy frames collected with the dynamic pulmonary imaging (DPI) technique.

In particular, I shall present a novel multiresolutional method with an explicit ventilation/perfusion analysis model, as well as "United Snakes," an interactive deformable model framework for lung registration and motion analysis, cardiac shape and motion analysis, and other applications. Finally, I will introduce a fast yet effective concentration-oriented tobogganing technique for efficient local artery/vein separation and multiple instance classification for the automated detection of pulmonary embolism from CT pulmonary angiography (CTPA), and a virtual colonoscopy technique that simplifies the complex 3D-polyp detection problem into a 2D-disk identification problem, significantly improving sensitivity while reducing computation time.

Biography:
Dr. Jianming Liang is a staff scientist at Siemens Medical Solutions USA, Inc., where he has been engaged in research and development activities in the domain of computer-aided diagnosis in medical imaging since December 2002. He holds a PhD degree (2001) in computer science and carried out his thesis work at the Turku Centre for Computer Science in Finland and in the Visual Modeling Group at the University of Toronto in Canada. From 2001-02, he was a Natural Sciences and Engineering Research Council (NSERC) of Canada Industrial Research Fellow. His research on dynamic chest image analysis received a University Faculty Research Award from the University of Turku. His other prizes include a Siemens Recognition Award and a Best Paper Award at the 2007 International Congress of Computer Assisted Radiology and Surgery in Berlin, Germany.

4) Thursday, January 31st, at noon; location TBA
Daniel Rubin, MS, MD
Stanford University

Visiting Faculty: January 18, 2008

Myeong Sub Lee, MD, PhD, and Sun Mi Kim, MD, have been visiting professors of radiology since March of 2007; they will be visiting our Department for one year. Dr. Lee received his PhD from the Department of Anatomy at Korea University of Seoul, Korea, and his MD from Yonsei University, where he is an associate professor in the Department of Radiology at Yonsei Wonju Medical School. His specialty is interventional neuroradiology, and he works with Dr. Marks in the division of interventional neuroradiology at Stanford. After completing her residency and a fellowship at the Asan Medical Center in Seoul, Dr Kim became an assistant professor of Seoul National University Bundang Hospital where she specializes in breast imaging, particularly mammography and ultrasound. Working as a visiting professor at Stanford, Dr. Kim has had the opportunity to interpret breast MRI cases and conduct research with Dr. Bruce Daniel. When they are not working, Drs. Lee and Kim enjoy spending time with their little boy, Jaewon.

Quick Stats: Fastest Growing Modalities (SHC)

Thanks to Darryl Costales, reimbursement manager, for compiling these statistics. Please note that the CT, MR, and mammography exam volumes from 2001 to 2006 were compiled using IDX Rad while the 2007 exam volumes for each of these modalities were compiled using Web Focus.

CT and MR are the two fastest growing modalities at SHC. In 2007, we completed 53,363 CT studies and 18,661 MR exams at SHC, which represent a 62.54%% and 42.88% change in growth, respectively, from 2001.

Announcements II: January 15, 2008

Special Seminar Series on Radiological Informatics: As part of a special series on radiological informatics, we are offering seminars on Jan. 23rd, 24th, 28th, and 31st. Please watch future announcements for each seminar's title and abstract. In addition to the seminars listed below, there will be a few more talks, which are being scheduled now and will be posted shortly. Please contact Dr. Sandy Napel for more information.

1) Wednesday, January 23rd, at noon; location TBA
Julia Patriarche, PhD
Mayo Clinic

Title:
"Detection of Change in Serial Magnetic Resonance Studies of Brain Tumor Patients"

Abstract:
The comparison of serial magnetic resonance imaging studies is a common task in clinical radiology. It is, however, widely considered not to be very reproducible. There are a variety of reasons for this, including the confounding of disease-related changes with acquisition-related changes and issues related to information presentation. We have constructed a computational system that performs the comparison of serial magnetic resonance imaging studies and presents changes in the form of a color-coded change map, superimposed on the anatomical images. The system additionally formats the output as a quantitative summary. We used this quantitative summary to conduct a study with 88 brain tumor serial comparisons. Our results were suggestive that it may be possible to use the change detector to identify cancer changes months earlier than is possible using manual inspection, alone.

We have recently implemented an integrated system for the change detector, which includes a graphical user interface (GUI). The GUI not only displays the color-coded change map, but also allows the user to turn it on and off. The GUI provides linked cursors, and it additionally provides "flicker" functionality to allow the user to rapidly alternate between the serial acquisitions. We are preparing to deploy the GUI change detector clinically, which will greatly increase the size and variety of possible future research studies and which will allow the direct clinical application of this technology.

The change detector is an example of a layered artificial intelligence (AI) architecture in which each layer builds upon the layer below, with each layer accomplishing progressively more sophisticated analyses. Specifically, the change detector is built on a lesion-finder application. The lesion finder is built on an automated sample point's algorithm. The automated sample point's algorithm is built on a significant region detection algorithm. Each of these algorithms has merit in its own right, and each can be used in a modular fashion in a variety of contexts. As a unified application, they together automatically address a complex clinical task. Early detection of changes may facilitate improved care through more rapid intervention following recurrence. It may also facilitate screening and personalized therapy. We additionally see the change detector as providing a solution to the problem of novel therapy comparison, by providing fully automatic, reproducible, and quantitative measures of change. We envision the change detector as a model of layered artificial intelligence, not only freeing the radiologist from the drudgery of information overload, but providing a model whereby greater information will enable many sophisticated automatic analyses by the computer, with the computer bringing to the attention of the clinician only what is relevant.

Biography:
Julia Patriarche is an informatics fellow in the Radiology Informatics Lab at the Mayo Clinic College of Medicine. She has completed an undergraduate degree in electrical engineering/computer engineering option at Queen's University in Kingston, Canada; a PhD in medical science/medical imaging; and a neurology fellowship at the Mayo Clinic College of Medicine.

2) Thursday, January 24th, at noon; location TBA
Ross Mitchell, PhD
University of Calgary

Title:
"Virtual Biopsies: Non-Invasive Molecular Diagnosis"

Abstract:
Our expanding knowledge of the genetic basis and molecular mechanisms of cancer is beginning to revolutionize the practice of clinical oncology. Increasingly, molecular biomarkers of prognosis and treatment response are being used to classify tumors and direct treatment decisions. Advanced medical imaging platforms such as MRI, PET, and CT provide incredibly detailed images of tumors that reflect their structure, biochemistry, physiology, and perhaps genetics.

Studies by the Imaging Informatics Lab at the University of Calgary, and others, show that information about a tumor's molecular phenotype can be obtained by using novel algorithms and computational tools to more fully analyze tumor images. Such "virtual biopsies," performed by applying these image-processing techniques to routine diagnostic images (e.g. MRI, PET, or CT), could be a rapid and powerful means of assaying important cancer biomarkers. If successfully validated, and proven to have suitable sensitivity and specificity, the use of non-invasive, imaging-based molecular diagnostic tests would offer significant advantages over conventional surgical biopsies. For example, this could be important in the context of large heterogeneous tumors, multiple metastases, surgically inaccessible tumors, and settings where disease progression needs to be monitored frequently over time. Virtual biopsy research lies at the intersection of molecular imaging, medical imaging physics, and biocomputation, and is highly complementary to these areas. This presentation will cover key enabling technologies behind virtual biopsies and discuss some recent successes in this research.

Biography: Dr. Ross Mitchell is an associate professor of the Departments of Radiology and Clinical Neurosciences and an adjunct professor of the Department of Computer Science at the University of Calgary. He is also the founding and chief scientist of Calgary Scientific Incorporated, a Multiple Sclerosis Society of Canada; a Donald Paty Scholar; and an Alberta Heritage Foundation for Medical Research Senior Scholar. Dr. Mitchell has received numerous awards for his research including the Berlex Canada MS Research Award; several Dean's Awards of Excellence from the University of Western Ontario; Best Paper Awards from the Canadian Association of Radiologists and the International Organization for Medical Physics; and two Awards of Merit from the Radiological Society of North America. Dr. Mitchell has a proven research track-record comprising 11 patents, 73 invited presentations, 63 peer-reviewed articles, and 150 published abstracts.

Dr. Mitchell supervises a research team investigating space/frequency analysis, medical image processing, as well as segmentation and visualization technologies. For more information, please see, http://www.ImagingInformatics.ca.

3) Monday, January 28th, at noon; location TBA
Jianming Liang, PhD
Siemens Medical Solutions

4) Thursday, January 31st, at noon; location TBA
Daniel Rubin, MS, MD
Stanford University

Digital Portablemania

By John Picard, FACHE, MPA, BSHA, RT(R)

During the month of September 2007, the SHC Radiology Department received delivery of two digital portable x-ray units courtesy of the SHC Emergency Department. These digital units have a screen integrated into the control panel that permits the viewing of images immediately after exposure. While the learning curve for this cutting-edge technology was longer than anticipated, the machines are now in full use. These machines are the first of their kind at SHC and will be used primarily to expedite patient care in the SHC ER. The SHC Radiology Department is very thankful and proud of the partnership with the SHC ER that enabled the acquisition of this technology.

People and Their Pets: Griz, Merley, and SpyderGurl

By Scott Evans, Gale Evan's Husband

These are our three pets, Griz, Merley, and SpyderGurl. Their stories, in order of seniority, are as follows . . .

Grizzly is the oldest and biggest member of our animal family. He is a purebred Australian Shepherd, and he is going to be thirteen years old this May. His coloring is the typical "tri-color" for Aussies--black, white, and gold. He is an extremely large example of the breed, being over 85 pounds in body weight.

Merley is the next in seniority, but he is only a year and a half old. He is also an Australian Shepherd, but there is some question as to the purity of his lineage. His coloring is also tri-color, but he is black, gold, and blue merle, hence his merle-y name. He was 52 pounds at his last checkup.

The newest member of our animal family is the kitty, whose official name is SpyderGurl but who most often answers to "Girlie-girl" or "Kit-kit-kittieohhhhhh." Her birthday is one day before Gale's, and she shares the same lovable and frustrating characteristics of most Aries. She is only eight months old and still developing her final size. She is about six pounds now.

Griz was born in Illinois and has had the life of a traveler until recently. With his size, color, and bobbed tail, we have to be careful at our house up North because he looks like a small bear when we are out walking. He is by far the best dog we have had in either of our lives, and we love him from the bottom of our hearts. That said, he is starting to stretch the limits of longevity for such a large dog and his physical strength is starting to wane. We noticed this last year and decided that we wanted to give him the chance to pass on his legacy to another member of his breed. We specifically timed Merley's birth to allow Griz the chance to mentor him in the proper ways of dog-dom.

Merley is learning well from Griz. His life is more about the pursuit of the next thrown tennis ball or how to shake the life from the Frisbee than lying about on the deck, but his manners are directly influenced by the Senior Statesman. He enjoys our walks in the morning, and he doesn't even mind leaving the toys at home as there are enough sniffing posts to keep his imagination occupied.

The Queen Apparent of the family is SpyderGurl. It didn't take too long for her to find the way to our hearts. She is an incurable snuggler (and hog of the bed). She enjoys being the center of attention but will only put up with so much petting before she needs to exercise her teeth and claws on your hand. She just got a new set of false fingernails to help with that problem. The best thing is that her favorite toy is an aluminum foil ball, which provides hours of cheap entertainment.

Pet animals have always been a part of our lives. We hope you enjoy the story of our current group.

New Faculty Hires and Promotions: January 10, 2008

John MacKenzie, MD, MS, became an acting assistant professor of pediatric radiology and chief of pediatric musculoskeletal imaging at Lucile Packard Children's Hospital (LPCH) in September of 2007. At LPCH, he is helping to expand the options for imaging and image-guided interventions for children, and he is excited to be back on the Farm. After completing his Bachelor of Science degree at Stanford with honors in computer science and the biological sciences, Dr. MacKenzie left Stanford for medical school at the Albert Einstein College of Medicine in the Bronx, which was initially a culture shock for him. However, Dr. MacKenzie enjoyed the East Coast enough to complete his residency at Brigham and Women's Hospital and two fellowships: a musculoskeletal and body MRI fellowship at the Hospital of the University of Pennsylvania and a pediatric radiology fellowship at Children's Hospital of Philadelphia. His research interests include molecular imaging applications for bone and joint disorders, and he is currently developing a research program in hyperpolarized carbon-13 imaging with members of Stanford Radiology (Drs. Dan Spielman, Shreyas Vasanawala, and Dirk Mayer) and General Electric (Ralph Hurd and Yi-Fen Yen). When he's not working, you may see him riding his green bike around campus reliving his undergraduate days as well as commuting to and from Caltrain. Dr. MacKenzie lives in San Francisco and enjoys hiking and carpentry; both his father and grandfather were carpenters. A native of Colorado, Dr. MacKenzie is currently teaching his seven-year-old daughter how to ice skate.

Announcements I: January 10, 2008

Special Seminar Series on Radiological Informatics: As part of a special series on radiological informatics, we are offering seminars on Jan. 14th, 23rd, and 28th. Each seminar is at 12 noon in Alway M104 unless otherwise indicated. Please watch future announcements for each seminar's title and abstract. In addition to the three seminars listed below, there will be at least two more talks, which are being scheduled now and will be posted shortly. Please contact Dr. Sandy Napel for more information.

1) Monday, Jan 14th:
James Z. Wang, PhD
Carnegie Mellon University and Pennsylvania State University

Title:
"A Data-Driven Approach Toward Knowledge Discovery and Improving Healthcare"

Abstract:
Radiology and biomedical informatics are revolutionizing healthcare. It has been predicted that a shortage of trained radiologists will continue in the next three decades. Effective computerized tools will therefore be in great demand. Radiology departments today generate an incredibly massive amount of digital medical images and metadata. Conventional PACS search methods allow physicians to locate images using metadata stored in relational databases. Much more can be done to leverage this wealth of data. Using massively parallel computers, we can mine millions of electronic medical records and millions of high-resolution, high-dimensional, multi-spectrum medical images to draw conclusions statistically based on past cases. We need to invent computational methods to harness the breathtaking quantity of digital information effectively and to generate biomedical knowledge at a pace we could not have imagined. In the last decade, my research group attempted to reduce the significant gap between low-level features extracted from images and high-level semantic concepts. Machine learning, statistical modeling, and mathematical tools have been utilized. I will introduce some of our past research results of relevance to the radiology community. Specifically, the talk will cover the SIMPLIcity visual similarity search, the 3-D hidden Markov models for analyzing volume images, the Automatic Linguistic Indexing of Pictures system, and the ontology-based annotation and retrieval of histological images and quantitative phenotypes. In the coming years, I plan to collaborate with radiologists, physicians, and biologists in order to develop indexing, retrieval, and mining algorithms and systems for large amounts of radiological images and patient-specific data.

Biography:
James Z. Wang is currently a visiting professor at the Robotics Institute of Carnegie Mellon University. He is also a tenured faculty member at Pennsylvania State University. He received a summa cum laude bachelor's degree in mathematics and computer science from the University of Minnesota. From Stanford University, Dr. Wang has received an MS in mathematics, an MS in computer science, and a PhD degree in medical information sciences. He has been a recipient of a National Science Foundation (NSF) Career award and the endowed PNC Technologies Career Development Professorship. Research interests of his group include automatic image tagging, semantics-sensitive image retrieval, image security, biomedical informatics, computational aesthetics, story picturing, art image retrieval, and computer vision. The group has published two monographs and more than 20 journal articles. Science media including Discovery News, Scientific American, National Public Radio, and MIT Technology Review, as well as wired news agencies, have reported his research.

2) Wednesday, January 23rd:
Julia Patriarche, PhD
Mayo Clinic

3) Monday, January 28th:
Jianming Liang, PhD
Siemens Medical Solutions

2007 MIPS Retreat

On October 24th, the Molecular Imaging Program at Stanford (MIPS) held a two-day retreat by the beach at Asilomar in Pacific Grove, California. Dr. Sam Gambhir, professor of radiology and bioengineering, director of the Molecular Imaging Program at Stanford, and chief of the Nuclear Medicine Division, gave the opening remarks, which were followed by lunch and one-minute talks moderated by Michael Moseley, PhD. Professor of Chemistry Carolyn Bertozzi from the University of California, Berkeley, delivered the keynote address entitled "Shedding Light on Glycans." The retreat also included discussion groups, a game show, and a faculty volleyball game.

Dr. Gambhir delivers the opening talk.

RSL/MIPS Thanksgiving Potluck Feast

For the first time ever, the Radiological Sciences Laboratory (RSL) and the Molecular Imaging Program at Stanford (MIPS) had a joint Thanksgiving potluck feast on November 21, 2007! Fred Chin, PhD, head of cyclotron radiochemistry, and Lin Davis, administrative associate, did an excellent job organizing and cooking for our feast. They fried four turkeys in peanut oil, which were delicious!

To see pictures from our event, please access photos taken by Dr. Sandip Biswal at
http://share.shutterfly.com/action/welcome?sid=0AaNG7ZqzbN2Luo&emid=sharview&linkid=link2 and Sharon Pollio at http://share.shutterfly.com/action/welcome?sid=0AaNG7ZqzbN2LxQ.